Popular model-free algorithms often use either random actions (epsilon-greedy approach), additive random noise (as in DDPG [1]}), or regularization of action distribution entropy (as in SAC [2]}). However, all these methods do not aim to maximize the novelty of the agent's information explicitly. Therefore, they do not work for environments where it is necessary to commit a specific sequence of actions to reach specific parts of state space.
